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The unabated growth and increasing significance of the World Wide Web has resulted in a flurry of research activity to improve its capacity for serving information more effectively. But at the heart of these efforts lie implicit assumptions about "quality" and "usefulness" of Web resources and services. This observation points towards measurements and models that quantify various attributes of web sites. The science of measuring all aspects of information, especially its storage and retrieval or informetrics has interested information scientists for decades before the existence of the Web. Is Web informetrics any different, or is it just an application of classical informetrics to a new medium? In this article, we examine this issue by classifying and discussing a wide ranging set of Web metrics. We present the origins, measurement functions, formulations and comparisons of well-known Web metrics for quantifying Web graph properties , Web page significance , Web page similarity , search and retrieval , usage characterization and information theoretic properties . We also discuss how these metrics can be applied for improving Web information access and use.

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Association rule mining is a well-researched area where many Wee-Keong Ng algorithms have been proposed to improve the speed of mining. In this paper, we propose an innovative algorithm called Rapid Association Rule Mining (RARM) to once again break this speed barrier. It uses a versatile tree structure known as the Szlpport-Ordered Die Ztemset (SOTrieIT) structure to hold pre-processed transactional data. This allows RARM to generate large l-itemsets and 2-itemsets quickly without scanning the database and without candidate 2-itemset generation. It achieves significant speed-ups because the main bottleneck in association rule mining using the Apriori property is the generation of candidate 2-itemsets. RARM has been compared with the classical mining algorithm Apriori and it is found that it outperforms Apriori by up to two orders of magnitude (100 times), much more than what recent mining algorithms are able to achieve.

Gradient descent is a widely used paradigm for solving many optimization problems. Stochastic gradient descent performs a series of iterations to minimize a target function in order to reach a local minimum. In machine learning or data mining, this function corresponds to a decision model that is to be discovered. The gradient descent paradigm underlies many commonly used techniques in data mining and machine learning, such as neural networks, Bayesian networks, genetic algorithms, and simulated annealing. To the best of our knowledge, there has not been any work that extends the notion of privacy preservation or secure multiparty computation to gradient-descent-based techniques. In this paper, we propose a preliminary approach to enable privacy preservation in gradient descent methods in general and demonstrate its feasibility in specific gradient descent methods.